Time series regression models have been widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this paper, we study the application of the quasi-least squares method to estimate the...

The inference about the population mean based on the standard t-test involves the assumption of normal population as well as independence of the observations. In this paper we examine the robustness of the inference in the presence of correlations among the observations. We consider the simplest...

For the data from multivariate t distributions, it is very hard to make an influence analysis based on the probability density function since its expression is intractable. In this paper, we present a technique for influence analysis based on the mixture distribution and EM algorithm. In fact,...

Count data with excess zeros often occurs in areas such as public health, epidemiology, psychology, sociology, engineering, and agriculture. Zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression are useful for modeling such data, but because of hierarchical...

Classical factor analysis relies on the assumption of normally distributed factors that guarantees the model to be estimated via the maximum likelihood method. Even when the assumption of Gaussian factors is not explicitly formulated and estimation is performed via the iterated principal...

Biased sampling from an underlying distribution with p.d.f. f(t), t0, implies that observations follow the weighted distribution with p.d.f. fw(t)=w(t)f(t)/E[w(T)] for a known weight function w. In particular, the function w(t)=tα has important applications, including length-biased sampling...

This work shows a procedure that aims to eliminate or reduce the bias caused by omitted variables by means of the so-called regime-switching regressions. There is a bias estimation whenever the statistical (linear) model is under-specified, that is, when there are some omitted variables and they...

This article uses several approaches to deal with the difficulty involved in evaluating the intractable integral when using Gibbs sampling to estimate the nonlinear mixed effects model (NLMM) based on the Dirichlet process (DP). For illustration, we applied these approaches to real data and...

Standard discriminant analysis supposes that both the training sample and the test sample are derived from the same population. When these samples arise from populations differing in their descriptive parameters, a generalization of discriminant analysis consists of adapting the classification...

We propose Bayesian methods with five types of priors to estimate cell probabilities in an incomplete multi-way contingency table under nonignorable nonresponse. In this situation, the maximum likelihood (ML) estimates often fall in the boundary solution, causing the ML estimates to become...